Semi-supervised learning (SSL) has been proven to be a powerful method for
leveraging unlabelled data to alleviate models' dependence on large labelled
datasets. The common framework among recent approaches is to train the model on
a large amount of unlabelled data with consistency regularization to constrain
the model predictions to be invariant to input perturbation. However, the
existing SSL frameworks still have room for improvement in the consistency
regularization method. Instead of regularizing category predictions in the
label space as in existing frameworks, this paper proposes a feature space
renormalization (FSR) mechanism for SSL. First, we propose a feature space
renormalization mechanism to substitute for the commonly used consistency
regularization mechanism to learn better discriminative features. To apply this
mechanism, we start by building a basic model and an empirical model and then
introduce our mechanism to renormalize the feature learning of the basic model
with the guidance of the empirical model. Second, we combine the proposed
mechanism with pseudo-labelling to obtain a novel effective SSL model named
FreMatch. The experimental results show that our method can achieve better
performance on a variety of standard SSL benchmark datasets, and the proposed
feature space renormalization mechanism can also enhance the performance of
other SSL approaches.

本文提出了一种特征空间重整化机制 (FSR) 作为半监督学习的一种代替一致性正则化方法的方法，并将其与伪标签相结合，得到了一种名为 FreMatch 的新型有效的半监督学习模型，实验证明该方法在多种标准半监督学习基准数据集上能够取得更好的性能，并且所提出的特征空间重整化机制还能增强其他半监督学习方法的性能。